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<a href="_assets_library_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#ifndef __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#define __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_coordinates_8h.xhtml">arm_compute/core/Coordinates.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_tensor_info_8h.xhtml">arm_compute/core/TensorInfo.h</a>&quot;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_tensor_shape_8h.xhtml">arm_compute/core/TensorShape.h</a>&quot;</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>&quot;</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_window_8h.xhtml">arm_compute/core/Window.h</a>&quot;</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="preprocessor">#include &quot;libnpy/npy.hpp&quot;</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_raw_tensor_8h.xhtml">tests/RawTensor.h</a>&quot;</span></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_tensor_cache_8h.xhtml">tests/TensorCache.h</a>&quot;</span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2_utils_8h.xhtml">tests/Utils.h</a>&quot;</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_exceptions_8h.xhtml">tests/framework/Exceptions.h</a>&quot;</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#include &lt;cstddef&gt;</span></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;fstream&gt;</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;random&gt;</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;string&gt;</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;type_traits&gt;</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;vector&gt;</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;{</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="keyword">namespace </span>test</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;{</div><div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml">   58</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml">AssetsLibrary</a> final</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;{</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#adb53338108890e6b7354e16a1e9ae716">AssetsLibrary</a>(std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a>, std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>);</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#acc474b96886b5fd500460c7b25dc84fa">get_image_shape</a>(<span class="keyword">const</span> std::string &amp;name);</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name) <span class="keyword">const</span>;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>, <span class="keywordtype">int</span> num_channels = 1) <span class="keyword">const</span>;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel);</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel);</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">fill_borders_with_garbage</a>(T &amp;&amp;tensor, D &amp;&amp;distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &amp;&amp;tensor, D &amp;&amp;distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw, D &amp;&amp;distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &amp;&amp;tensor, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &amp;&amp;tensor, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &amp;&amp;tensor, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &amp;&amp;tensor, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw) <span class="keyword">const</span>;</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(T &amp;&amp;tensor, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(T &amp;&amp;tensor, std::random_device::result_type seed_offset, D low, D high) <span class="keyword">const</span>;</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">fill_layer_data</a>(T &amp;&amp;tensor, std::string name) <span class="keyword">const</span>;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">fill_tensor_value</a>(T &amp;&amp;tensor, D value) <span class="keyword">const</span>;</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="comment">// Function prototype to convert between image formats.</span></div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    <span class="keyword">using</span> Converter = void (*)(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <span class="comment">// Function prototype to extract a channel from an image.</span></div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    <span class="keyword">using</span> Extractor = void (*)(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    <span class="comment">// Function prototype to load an image file.</span></div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    <span class="keyword">using</span> Loader = <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> (*)(<span class="keyword">const</span> std::string &amp;<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a>);</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keyword">const</span> Converter &amp;get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>) <span class="keyword">const</span>;</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="keyword">const</span> Converter &amp;get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    <span class="keyword">const</span> Converter &amp;get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    <span class="keyword">const</span> Converter &amp;get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="keyword">const</span> Extractor &amp;get_extractor(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a>) <span class="keyword">const</span>;</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keyword">const</span> Loader &amp;get_loader(<span class="keyword">const</span> std::string &amp;extension) <span class="keyword">const</span>;</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> load_image(<span class="keyword">const</span> std::string &amp;name) <span class="keyword">const</span>;</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;find_or_create_raw_tensor(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;find_or_create_raw_tensor(<span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;    <span class="keyword">mutable</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">TensorCache</a>             _cache{};</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;    <span class="keyword">mutable</span> std::mutex              _format_lock{};</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;    <span class="keyword">mutable</span> std::mutex              _channel_lock{};</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    <span class="keyword">const</span> std::string               _library_path;</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    std::random_device::result_type _seed;</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;};</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00424"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">  424</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">AssetsLibrary::fill_borders_with_garbage</a>(T &amp;&amp;tensor, D &amp;&amp;distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_border_size.xhtml">PaddingSize</a> padding_size = tensor.padding();</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;    <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(0, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(-padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a05374b750b0fc472c34ee61e6f028bba">left</a>, tensor.shape()[0] + padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a78b0fed184c642b78f32fd34b228a5f9">right</a>, 1));</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <span class="keywordflow">if</span>(tensor.shape().num_dimensions() &gt; 1)</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    {</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;        window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(1, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(-padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a3fdd42ea34070a54e696b3adc28c4be3">top</a>, tensor.shape()[1] + padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">bottom</a>, 1));</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;    }</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    std::mt19937 gen(_seed);</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&amp;](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &amp; <span class="keywordtype">id</span>)</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    {</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a> = tensor.shape();</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        <span class="comment">// If outside of valid region</span></div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        <span class="keywordflow">if</span>(<span class="keywordtype">id</span>.x() &lt; 0 || <span class="keywordtype">id</span>.x() &gt;= static_cast&lt;int&gt;(shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">x</a>()) || <span class="keywordtype">id</span>.y() &lt; 0 || <span class="keywordtype">id</span>.y() &gt;= <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a691c9cb93365c2e33f3429de43244098">y</a>()))</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        {</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;            <span class="keyword">using</span> ResultType         = <span class="keyword">typename</span> std::remove_reference&lt;D&gt;::type::result_type;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;            <span class="keyword">const</span> ResultType value   = distribution(gen);</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;            <span class="keywordtype">void</span> *<span class="keyword">const</span>      out_ptr = tensor(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(out_ptr, value, tensor.data_type());</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;        }</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;    });</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;}</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00453"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">  453</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &amp;&amp;tensor, D &amp;&amp;distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    <span class="keyword">using</span> ResultType = <span class="keyword">typename</span> std::remove_reference&lt;D&gt;::type::result_type;</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span>  is_nhwc = tensor.data_layout() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>(tensor.shape());</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    <span class="keywordflow">if</span>(is_nhwc)</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    {</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;        <span class="comment">// Ensure that the equivalent tensors will be filled for both data layouts</span></div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    }</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    <span class="comment">// Iterate over all elements</span></div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">int</span> element_idx = 0; element_idx &lt; tensor.num_elements(); ++element_idx)</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    {</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;        <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, element_idx);</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        <span class="keywordflow">if</span>(is_nhwc)</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;        {</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;            <span class="comment">// Write in the correct id for permuted shapes</span></div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;            <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(<span class="keywordtype">id</span>, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        }</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;        <span class="comment">// Iterate over all channels</span></div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">int</span> channel = 0; channel &lt; tensor.num_channels(); ++channel)</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        {</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;            <span class="keyword">const</span> ResultType value        = distribution(gen);</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;            ResultType      &amp;target_value = <span class="keyword">reinterpret_cast&lt;</span>ResultType *const<span class="keyword">&gt;</span>(tensor(<span class="keywordtype">id</span>))[channel];</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&amp;target_value, value, tensor.data_type());</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        }</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    }</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">fill_borders_with_garbage</a>(tensor, distribution, seed_offset);</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;}</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00493"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a935d861fcffb0099b001498c494daaf6">  493</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw, D &amp;&amp;distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> &lt; raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    {</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        <span class="keyword">using</span> ResultType       = <span class="keyword">typename</span> std::remove_reference&lt;D&gt;::type::result_type;</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;        <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, value, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>());</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    }</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;}</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00506"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a5ee4fc10b84f941236df524f618b96c6">  506</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &amp;&amp;tensor, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)<span class="keyword"> const</span></div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw = <span class="keyword">get</span>(name, format);</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> &lt; raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    {</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>());</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span>                         out_ptr = <span class="keyword">static_cast&lt;</span><a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">&gt;</span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;        std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>(), out_ptr);</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    }</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;}</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00521"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4d049b9a5aba1f30b51f4bd36c3db076">  521</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &amp;&amp;tensor, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(std::forward&lt;T&gt;(tensor), name, <a class="code" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a>(channel), channel);</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;}</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00527"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a36a9ddc6792949fb561cd788e6e31208">  527</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &amp;&amp;tensor, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;raw = <span class="keyword">get</span>(name, format, channel);</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> &lt; raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    {</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>());</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span>                         out_ptr = <span class="keyword">static_cast&lt;</span><a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">&gt;</span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;        std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>(), out_ptr);</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;    }</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;}</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00542"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a7eb916e4bf718e682ae89cb99aa348f9">  542</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &amp;&amp;tensor, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw)<span class="keyword"> const</span></div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> &lt; raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    {</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>());</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span>                         out_ptr = <span class="keyword">static_cast&lt;</span><a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">&gt;</span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;        std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>(), out_ptr);</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    }</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;}</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00555"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">  555</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">AssetsLibrary::fill_tensor_uniform</a>(T &amp;&amp;tensor, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;    <span class="keywordflow">switch</span>(tensor.data_type())</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    {</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;        {</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;            std::uniform_int_distribution&lt;uint8_t&gt; distribution_u8(std::numeric_limits&lt;uint8_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;uint8_t&gt;::max</a>());</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;        }</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;        {</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;            std::uniform_int_distribution&lt;int8_t&gt; distribution_s8(std::numeric_limits&lt;int8_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;int8_t&gt;::max</a>());</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;        }</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;        {</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;            std::uniform_int_distribution&lt;uint16_t&gt; distribution_u16(std::numeric_limits&lt;uint16_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;uint16_t&gt;::max</a>());</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;        }</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>:</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;        {</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;            std::uniform_int_distribution&lt;int16_t&gt; distribution_s16(std::numeric_limits&lt;int16_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;int16_t&gt;::max</a>());</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;        }</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;        {</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;            std::uniform_int_distribution&lt;uint32_t&gt; distribution_u32(std::numeric_limits&lt;uint32_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;uint32_t&gt;::max</a>());</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;        }</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;        {</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;            std::uniform_int_distribution&lt;int32_t&gt; distribution_s32(std::numeric_limits&lt;int32_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;int32_t&gt;::max</a>());</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;        }</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;        {</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;            std::uniform_int_distribution&lt;uint64_t&gt; distribution_u64(std::numeric_limits&lt;uint64_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;uint64_t&gt;::max</a>());</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;        }</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;        {</div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;            std::uniform_int_distribution&lt;int64_t&gt; distribution_s64(std::numeric_limits&lt;int64_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;int64_t&gt;::max</a>());</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;        }</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;        {</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;            <span class="comment">// It doesn&#39;t make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;            std::uniform_real_distribution&lt;float&gt; distribution_f16(-100.f, 100.f);</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;        }</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;        {</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;            <span class="comment">// It doesn&#39;t make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;            std::uniform_real_distribution&lt;float&gt; distribution_f32(-1000.f, 1000.f);</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;        }</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;        {</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;            <span class="comment">// It doesn&#39;t make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;            std::uniform_real_distribution&lt;double&gt; distribution_f64(-1000.f, 1000.f);</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;        }</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;        {</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;            std::uniform_int_distribution&lt;size_t&gt; distribution_sizet(std::numeric_limits&lt;size_t&gt;::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits&lt;size_t&gt;::max</a>());</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;        }</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;            <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">&quot;NOT SUPPORTED!&quot;</span>);</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    }</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;}</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00643"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a150d2023c197e197f09f350ede795085">  643</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">AssetsLibrary::fill_tensor_uniform</a>(T &amp;&amp;tensor, std::random_device::result_type seed_offset, D low, D high)<span class="keyword"> const</span></div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;    <span class="keywordflow">switch</span>(tensor.data_type())</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;    {</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;        {</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;uint8_t, D&gt;::value));</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;            std::uniform_int_distribution&lt;uint8_t&gt; distribution_u8(low, high);</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;        }</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;        {</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;int8_t, D&gt;::value));</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;            std::uniform_int_distribution&lt;int8_t&gt; distribution_s8(low, high);</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;        }</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;        {</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;uint16_t, D&gt;::value));</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;            std::uniform_int_distribution&lt;uint16_t&gt; distribution_u16(low, high);</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        }</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>:</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;        {</div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;int16_t, D&gt;::value));</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;            std::uniform_int_distribution&lt;int16_t&gt; distribution_s16(low, high);</div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;        }</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;        {</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;uint32_t, D&gt;::value));</div><div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;            std::uniform_int_distribution&lt;uint32_t&gt; distribution_u32(low, high);</div><div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;        }</div><div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;        {</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;int32_t, D&gt;::value));</div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;            std::uniform_int_distribution&lt;int32_t&gt; distribution_s32(low, high);</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;        }</div><div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;        {</div><div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;uint64_t, D&gt;::value));</div><div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;            std::uniform_int_distribution&lt;uint64_t&gt; distribution_u64(low, high);</div><div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;        }</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;        {</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;int64_t, D&gt;::value));</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;            std::uniform_int_distribution&lt;int64_t&gt; distribution_s64(low, high);</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;        }</div><div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;        {</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;            std::uniform_real_distribution&lt;float&gt; distribution_f16(low, high);</div><div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;        }</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;        {</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;float, D&gt;::value));</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;            std::uniform_real_distribution&lt;float&gt; distribution_f32(low, high);</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;        }</div><div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;        {</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;double, D&gt;::value));</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;            std::uniform_real_distribution&lt;double&gt; distribution_f64(low, high);</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;        }</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;        {</div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same&lt;size_t, D&gt;::value));</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;            std::uniform_int_distribution&lt;size_t&gt; distribution_sizet(low, high);</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;            <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;        }</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;            <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">&quot;NOT SUPPORTED!&quot;</span>);</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;    }</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;}</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00739"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">  739</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">AssetsLibrary::fill_layer_data</a>(T &amp;&amp;tensor, std::string name)<span class="keyword"> const</span></div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;<span class="preprocessor">#ifdef _WIN32</span></div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;    <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">&quot;\\&quot;</span>);</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;<span class="preprocessor">#else  </span><span class="comment">/* _WIN32 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;    <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">&quot;/&quot;</span>);</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* _WIN32 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;    <span class="keyword">const</span> std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a> = _library_path + path_separator + name;</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;    std::vector&lt;unsigned long&gt; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>;</div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;</div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;    <span class="comment">// Open file</span></div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    std::ifstream stream(path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;    <span class="keywordflow">if</span>(!stream.good())</div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;    {</div><div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarm__compute_1_1test_1_1framework_1_1_file_not_found.xhtml">framework::FileNotFound</a>(<span class="stringliteral">&quot;Could not load npy file: &quot;</span> + path);</div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;    }</div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;    std::string header = npy::read_header(stream);</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;</div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;    <span class="comment">// Parse header</span></div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;    <span class="keywordtype">bool</span>        fortran_order = <span class="keyword">false</span>;</div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;    std::string typestr;</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;    npy::parse_header(header, typestr, fortran_order, shape);</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;    <span class="comment">// Check if the typestring matches the given one</span></div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;    std::string expect_typestr = <a class="code" href="namespacearm__compute_1_1test.xhtml#ae47155d6186155ec4da9295764b3c05a">get_typestring</a>(tensor.data_type());</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;    <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(typestr != expect_typestr, <span class="stringliteral">&quot;Typestrings mismatch&quot;</span>);</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;    <span class="comment">// Validate tensor shape</span></div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;    <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(shape.size() != tensor.shape().num_dimensions(), <span class="stringliteral">&quot;Tensor ranks mismatch&quot;</span>);</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;    <span class="keywordflow">if</span>(fortran_order)</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;    {</div><div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i &lt; shape.size(); ++i)</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;        {</div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;            <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(tensor.shape()[i] != shape[i], <span class="stringliteral">&quot;Tensor dimensions mismatch&quot;</span>);</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;        }</div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;    }</div><div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;    {</div><div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i &lt; shape.size(); ++i)</div><div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;        {</div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;            <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(tensor.shape()[i] != shape[shape.size() - i - 1], <span class="stringliteral">&quot;Tensor dimensions mismatch&quot;</span>);</div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;        }</div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;    }</div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;</div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;    <span class="comment">// Read data</span></div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;    <span class="keywordflow">if</span>(tensor.padding().empty())</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;    {</div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;        <span class="comment">// If tensor has no padding read directly from stream.</span></div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;        stream.read(reinterpret_cast&lt;char *&gt;(tensor.data()), tensor.size());</div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;    }</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;    {</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;        <span class="comment">// If tensor has padding accessing tensor elements through execution window.</span></div><div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;        <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;        window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor.shape());</div><div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;</div><div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;        <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&amp;](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &amp; <span class="keywordtype">id</span>)</div><div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;        {</div><div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;            stream.read(reinterpret_cast&lt;char *&gt;(tensor(<span class="keywordtype">id</span>)), tensor.element_size());</div><div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;        });</div><div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;    }</div><div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;}</div><div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;</div><div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> D&gt;</div><div class="line"><a name="l00804"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">  804</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">AssetsLibrary::fill_tensor_value</a>(T &amp;&amp;tensor, D value)<span class="keyword"> const</span></div><div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00806"></a><span class="lineno">  806</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(tensor, 0, value, value);</div><div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;}</div><div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__ */</span><span class="preprocessor"></span></div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00260">Error.h:260</a></div></div>
<div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a3fdd42ea34070a54e696b3adc28c4be3"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a3fdd42ea34070a54e696b3adc28c4be3">arm_compute::BorderSize::top</a></div><div class="ttdeci">unsigned int top</div><div class="ttdoc">top of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00371">Types.h:371</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">arm_compute::DataType::QS16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number </div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00133">Convolution.cpp:133</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a62b67b578f684c4d516843c9dea86a23"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">arm_compute::test::SimpleTensor::element_size</a></div><div class="ttdeci">size_t element_size() const override</div><div class="ttdoc">Size of each element in the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00300">SimpleTensor.h:300</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">arm_compute::test::RawTensor</a></div><div class="ttdoc">Subclass of SimpleTensor using uint8_t as value type. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8h_source.xhtml#l00038">RawTensor.h:38</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_abb6f25295592e886976520216187eed7"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">arm_compute::test::AssetsLibrary::fill_tensor_uniform</a></div><div class="ttdeci">void fill_tensor_uniform(T &amp;&amp;tensor, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Fill a tensor with uniform distribution across the range of its type. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00555">AssetsLibrary.h:555</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_tensor_cache_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">arm_compute::test::TensorCache</a></div><div class="ttdoc">Stores RawTensor categorised by the image they are created from including name, format and channel...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_cache_8h_source.xhtml#l00040">TensorCache.h:40</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a80a7b5ae084bf22b91bc5f68a06711c0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">arm_compute::test::AssetsLibrary::fill</a></div><div class="ttdeci">void fill(T &amp;&amp;tensor, D &amp;&amp;distribution, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Fills the specified tensor with random values drawn from distribution. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00453">AssetsLibrary.h:453</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_ad7701a09a964eab360a8e51fa7ad2c16"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">arm_compute::test::SimpleTensor::size</a></div><div class="ttdeci">size_t size() const override</div><div class="ttdoc">Total size of the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00318">SimpleTensor.h:318</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::DataType::QS8</a></div><div class="ttdoc">quantized, symmetric fixed-point 8-bit number </div></div>
<div class="ttc" id="structarm__compute_1_1_border_size_xhtml"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml">arm_compute::BorderSize</a></div><div class="ttdoc">Container for 2D border size. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00291">Types.h:291</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml">arm_compute::test::AssetsLibrary</a></div><div class="ttdoc">Factory class to create and fill tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00058">AssetsLibrary.h:58</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_aa337ab76176f3c4193642ac6de3a61cf"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">arm_compute::test::get_format_for_channel</a></div><div class="ttdeci">Format get_format_for_channel(Channel channel)</div><div class="ttdoc">Look up the format corresponding to a channel. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00148">Utils.h:148</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::Format::U8</a></div><div class="ttdoc">1 channel, 1 U8 per channel </div></div>
<div class="ttc" id="_window_8h_xhtml"><div class="ttname"><a href="_window_8h.xhtml">Window.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00337">SimpleTensor.h:337</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00047">Types.h:47</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image&amp;#39;s dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::Format::U16</a></div><div class="ttdoc">1 channel, 1 U16 per channel </div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a4035a1140831801ced5dfa1d9fe6988a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">arm_compute::test::AssetsLibrary::seed</a></div><div class="ttdeci">std::random_device::result_type seed() const </div><div class="ttdoc">Seed that is used to fill tensors with random values. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00260">AssetsLibrary.cpp:260</a></div></div>
<div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a802ffcf1b49237efe5be8a314d3f3869"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">arm_compute::BorderSize::bottom</a></div><div class="ttdeci">unsigned int bottom</div><div class="ttdoc">bottom of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00373">Types.h:373</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00294">SimpleTensor.h:294</a></div></div>
<div class="ttc" id="_tensor_info_8h_xhtml"><div class="ttname"><a href="_tensor_info_8h.xhtml">TensorInfo.h</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00303">helpers.h:303</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_acc474b96886b5fd500460c7b25dc84fa"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#acc474b96886b5fd500460c7b25dc84fa">arm_compute::test::AssetsLibrary::get_image_shape</a></div><div class="ttdeci">TensorShape get_image_shape(const std::string &amp;name)</div><div class="ttdoc">Provides a tensor shape for the specified image. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00458">AssetsLibrary.cpp:458</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_a14470b4cb59140a1b6ff3b8f16c89ab6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const TensorShape &amp;shape, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor&amp;#39;s dimensions to fill the window dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00240">Window.inl:240</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a9b67b266207227062c7a2961ef85293a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">arm_compute::test::AssetsLibrary::path</a></div><div class="ttdeci">std::string path() const </div><div class="ttdoc">Path to assets directory used to initialise library. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00255">AssetsLibrary.cpp:255</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel </div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a9a6d9b68649fb7444ca36b5c651dfdda"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">arm_compute::test::AssetsLibrary::fill_borders_with_garbage</a></div><div class="ttdeci">void fill_borders_with_garbage(T &amp;&amp;tensor, D &amp;&amp;distribution, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Puts garbage values all around the tensor for testing purposes. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00424">AssetsLibrary.h:424</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_afb5cd37bb08f1029691590372e6330f0"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const </div><div class="ttdoc">Alias to access the size of the first dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions&lt; T &gt; &amp;dimensions, const PermutationVector &amp;perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00536">Helpers.h:536</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel </div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::DataType::S64</a></div><div class="ttdoc">signed 64-bit number </div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ad7f431e3446fddcd9b6b9f93c4c14"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00116">GEMM.cpp:116</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a1e6934e95738573214c2ce1d6648d116"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">arm_compute::test::store_value_with_data_type</a></div><div class="ttdeci">void store_value_with_data_type(void *ptr, T value, DataType data_type)</div><div class="ttdoc">Write the value after casting the pointer according to data_type. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00318">Utils.h:318</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">arm_compute::DataType::SIZET</a></div><div class="ttdoc">size_t </div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a0c52a8f0085b55d907af7210ef2069d0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">arm_compute::test::SimpleTensor::data</a></div><div class="ttdeci">const T * data() const </div><div class="ttdoc">Constant pointer to the underlying buffer. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00398">SimpleTensor.h:398</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 U32 per channel </div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel. </div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455a"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">arm_compute::Channel</a></div><div class="ttdeci">Channel</div><div class="ttdoc">Available channels. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00481">Types.h:481</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">arm_compute::Format</a></div><div class="ttdeci">Format</div><div class="ttdoc">Image colour formats. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00050">Types.h:50</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &amp;w, L &amp;&amp;lambda_function, Ts &amp;&amp;...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00122">Helpers.inl:122</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number </div></div>
<div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_afb9ded5f49336ae503bb9f2035ea902b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">arm_compute::test::SimpleTensor&lt; uint8_t &gt;::value_type</a></div><div class="ttdeci">uint8_t value_type</div><div class="ttdoc">Tensor value type. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00107">SimpleTensor.h:107</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1framework_1_1_file_not_found_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1framework_1_1_file_not_found.xhtml">arm_compute::test::framework::FileNotFound</a></div><div class="ttdoc">Error class for when some external assets are missing. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8h_source.xhtml#l00067">Exceptions.h:67</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00137">Convolution.cpp:137</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &amp;dim)</div><div class="ttdoc">Set the values of a given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00041">Window.inl:41</a></div></div>
<div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a24d8c0391cfa38e78969b6ad97c0ff09"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">arm_compute::test::index2coord</a></div><div class="ttdeci">Coordinates index2coord(const TensorShape &amp;shape, int index)</div><div class="ttdoc">Convert a linear index into n-dimensional coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00419">Utils.h:419</a></div></div>
<div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a05374b750b0fc472c34ee61e6f028bba"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a05374b750b0fc472c34ee61e6f028bba">arm_compute::BorderSize::left</a></div><div class="ttdeci">unsigned int left</div><div class="ttdoc">left of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00374">Types.h:374</a></div></div>
<div class="ttc" id="_tensor_cache_8h_xhtml"><div class="ttname"><a href="_tensor_cache_8h.xhtml">TensorCache.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a0457532b61fe412895684d12d08f5d0f"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">arm_compute::test::AssetsLibrary::fill_tensor_value</a></div><div class="ttdeci">void fill_tensor_value(T &amp;&amp;tensor, D value) const </div><div class="ttdoc">Fill a tensor with a constant value. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00804">AssetsLibrary.h:804</a></div></div>
<div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a78b0fed184c642b78f32fd34b228a5f9"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a78b0fed184c642b78f32fd34b228a5f9">arm_compute::BorderSize::right</a></div><div class="ttdeci">unsigned int right</div><div class="ttdoc">right of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00372">Types.h:372</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::Format::S16</a></div><div class="ttdoc">1 channel, 1 S16 per channel </div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_ae47155d6186155ec4da9295764b3c05a"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#ae47155d6186155ec4da9295764b3c05a">arm_compute::test::get_typestring</a></div><div class="ttdeci">std::string get_typestring(DataType data_type)</div><div class="ttdoc">Obtain numpy type string from DataType. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00688">Utils.h:688</a></div></div>
<div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_ad85dc4c57a27c44d114c573b9a80bad6"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">arm_compute::test::AssetsLibrary::fill_layer_data</a></div><div class="ttdeci">void fill_layer_data(T &amp;&amp;tensor, std::string name) const </div><div class="ttdoc">Fills the specified tensor with data loaded from .npy (numpy binary) in specified path...</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00739">AssetsLibrary.h:739</a></div></div>
<div class="ttc" id="_coordinates_8h_xhtml"><div class="ttname"><a href="_coordinates_8h.xhtml">Coordinates.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a691c9cb93365c2e33f3429de43244098"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a691c9cb93365c2e33f3429de43244098">arm_compute::Dimensions::y</a></div><div class="ttdeci">T y() const </div><div class="ttdoc">Alias to access the size of the second dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00086">Dimensions.h:86</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels. </div></div>
<div class="ttc" id="_exceptions_8h_xhtml"><div class="ttname"><a href="_exceptions_8h.xhtml">Exceptions.h</a></div></div>
<div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point&lt; T &gt; max(fixed_point&lt; T &gt; x, fixed_point&lt; T &gt; y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00902">FixedPoint.h:902</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::DataType::F64</a></div><div class="ttdoc">64-bit floating-point number </div></div>
<div class="ttc" id="_raw_tensor_8h_xhtml"><div class="ttname"><a href="_raw_tensor_8h.xhtml">RawTensor.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::DataType::U64</a></div><div class="ttdoc">unsigned 64-bit number </div></div>
<div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00072">Types.h:72</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div><div class="ttdoc">signed 8-bit number </div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_adb53338108890e6b7354e16a1e9ae716"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#adb53338108890e6b7354e16a1e9ae716">arm_compute::test::AssetsLibrary::AssetsLibrary</a></div><div class="ttdeci">AssetsLibrary(std::string path, std::random_device::result_type seed)</div><div class="ttdoc">Initialises the library with a path to the assets directory. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00249">AssetsLibrary.cpp:249</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure &amp; src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00147">Convolution.cpp:147</a></div></div>
<div class="ttc" id="_tensor_shape_8h_xhtml"><div class="ttname"><a href="_tensor_shape_8h.xhtml">TensorShape.h</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00319">Error.h:319</a></div></div>
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